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Tunnel Surface Settlement Forecasting with Ensemble Learning

Author

Listed:
  • Ke Yan

    (Department of Building, School of Design and Environment, National University of Singapore, Architecture Drive, Singapore 117566, Singapore)

  • Yuting Dai

    (Zhejiang Geely Holding Group Co., LTD. 1760, Jiangling Road, Binjiang District, Hangzhou 310051, China)

  • Meiling Xu

    (Nanhu College, Jiaxing University, Jiaxing 314001, China)

  • Yuchang Mo

    (Fujian Province University Key Laboratory of Computational Science, School of Mathematical Sciences, Huaqiao University, Quanzhou 362021, China)

Abstract

Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency.

Suggested Citation

  • Ke Yan & Yuting Dai & Meiling Xu & Yuchang Mo, 2019. "Tunnel Surface Settlement Forecasting with Ensemble Learning," Sustainability, MDPI, vol. 12(1), pages 1-11, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:232-:d:302368
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    References listed on IDEAS

    as
    1. Chaowen Zhong & Ke Yan & Yuting Dai & Ning Jin & Bing Lou, 2019. "Energy Efficiency Solutions for Buildings: Automated Fault Diagnosis of Air Handling Units Using Generative Adversarial Networks," Energies, MDPI, vol. 12(3), pages 1-11, February.
    2. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    3. Ke Yan & Xudong Wang & Yang Du & Ning Jin & Haichao Huang & Hangxia Zhou, 2018. "Multi-Step Short-Term Power Consumption Forecasting with a Hybrid Deep Learning Strategy," Energies, MDPI, vol. 11(11), pages 1-15, November.
    4. Yang Du & Ke Yan & Zixiao Ren & Weidong Xiao, 2018. "Designing Localized MPPT for PV Systems Using Fuzzy-Weighted Extreme Learning Machine," Energies, MDPI, vol. 11(10), pages 1-10, October.
    5. Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
    6. Wang, Huai-zhi & Li, Gang-qiang & Wang, Gui-bin & Peng, Jian-chun & Jiang, Hui & Liu, Yi-tao, 2017. "Deep learning based ensemble approach for probabilistic wind power forecasting," Applied Energy, Elsevier, vol. 188(C), pages 56-70.
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    Cited by:

    1. Qu, Pengfei & Zhang, Limao & Zhu, Qizhi & Wu, Maozhi, 2023. "Probabilistic reliability assessment of twin tunnels considering fluid–solid coupling with physics-guided machine learning," Reliability Engineering and System Safety, Elsevier, vol. 231(C).

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